November 16 - 19, 2026
AI Coding Summit
New York, US & Online

AI Coding Summit NYC

See how AI transforms software development

Full remote ticket included with Multipass.

Bringing AI coding best practices to NYC. Two days of practical talks and workshops on coding agents, AI code review, context engineering, prompt-driven development, and shipping production software with AI at scale. Connect with the East Coast AI developer community and learn proven workflows from engineering leaders who use AI tools daily.

LLM Knowledge Bases: How Agents Can Organize All of Your Notes
Upcoming
LLM Knowledge Bases: How Agents Can Organize All of Your Notes
Notetaking is a beautiful thing. Putting thoughts to paper (or keyboard, or transcription model) refines your thinking, connects ideas, and pulls context out of your brain for others to learn from.

But as powerful as a good notetaking system can be, organizing those notes is a chore. A few files turn into folders, tags, and taxonomies that become unwieldy beyond the first hundred entries. If you can't find what you wrote, or miss connections to related ideas, you're missing the real value of notetaking: generating new ideas.

Agents dramatically expanded what's possible here. Andre Karpathy popularized the idea of an "LLM Knowledge Base." Or in simpler terms, letting Claude Code turn your loose Markdown notes into a neat, organized wiki.

We'll explore workflows to make this possible, ranging from simple to complex:
- Build automations to organize your notes with tags, folders, backlinks, and deduplication to level-up search and discovery
- Teach agents to create a wiki you can navigate with Obsidian
- Expand from wikis to web applications to create kanban boards, knowledge graphs, whiteboard canvases, and more
- Have agents expand your thinking by auto-recording ideas while you sleep
- Connect third party data sources like Granola meeting notes or Notion databases to link your notes to your day-to-day work
You'll walk away with a new appreciation for notetaking, and a second brain that leaves you 10x smarter than one brain alone.

Talk format: Code and live tech demos. I will set up each of these automations and tools from scratch, and show agents executing each of them in a live setting. I will share the source for all automations at the end.
The Validation-First Loop: How to Ship Production Code with Claude Code
Upcoming
The Validation-First Loop: How to Ship Production Code with Claude Code
Coding agents like Claude Code barely write bad code anymore. They write good code that misses the point. The failure moved from code time, where the agent couldn't write the function, to intent time, where it wrote a perfectly fine function for a task that wasn't quite the one in your head. That's misalignment, not incompetence.

Our reflex is more review, more tests, more guardrails. All of it runs downstream of the failure. The miss already happened before any of those fire, so you're doing an autopsy instead of preventing anything.

This talk shows the fix: work backwards. Before your agent writes a single line, you and the agent together define concrete, measurable success criteria and the validation strategy that proves them. Only then does it implement. It's TDD repurposed for AI agents! The validation-first plan gives the AI agent an actual contract for the task at hand.

I'll walk through it live on a real codebase, in Claude Code, on an ordinary task: the naive version that passes every check and still does the wrong thing, the plan that makes "done" explicit, and the same task done validation-first. Then the part that compounds, which is that when a miss does slip through you don't just patch the bug, you evolve the system so the same class of mistakes goes away over time. You own the plan, the agent owns the typing.

Attendees leave with three things they can apply on their next ticket, with no new tooling required.
Orchestrating Agent Swarms: Automating Express→Next.js Migrations with Task Planning & QA Loops
Upcoming
Orchestrating Agent Swarms: Automating Express→Next.js Migrations with Task Planning & QA Loops
Workshop
James O'Reilly
James O'Reilly
The Problem
Enterprise engineering teams are burdened by aging Node.js/Express monoliths tightly coupled to fragile templates and untyped data layers. Manually refactoring these massive codebases into modern cloud-native architectures is notoriously slow, introduces severe regressions, and creates massive code review bottlenecks for developer workflows.

The Solution
Instead of manual rewrites, modern teams can harness advanced agentic-programming patterns to orchestrate dedicated agent swarms. This workshop guides advanced engineers through an automated refactoring and migration pipeline using Google Antigravity. Attendees will learn how to leverage structured task planning, parallel subagents, and automated LLM code reviews to transform a legacy Express monolith into a strongly-typed Next.js application.

Key Takeaways
By the end of this hands-on session, attendees will be able to:

1. Orchestrate Agent Swarms: Author deterministic task plans and custom Agent Skills that direct parallel subagents to audit legacy middleware, API contracts, and schemas.

2. Execute AI Refactoring & Migrations: Automate large-scale structural code generation while establishing rigorous LLM-assisted code review gates.

3. Implement AI-Assisted Testing & QA: Set up autonomous CI/CD verification loops where agents execute failing tests, parse error logs, and self-correct code until runtime parity is perfectly proven.
Register
Claude Code: Black Belt
Sep 9, 13:00
Claude Code: Black Belt
Workshop
Pawel Sawicki
Pawel Sawicki
Stop prompting. Start orchestrating. In four intense hours you'll go from using Claude Code like a faster autocomplete to commanding it like a senior engineer commands a team: engineering its context, deploying fleets of subagents, locking it down with hooks, and turning it loose on work that runs without you.Every Claude Code user hits a ceiling where the easy wins run out. The agent handles small stuff beautifully, then loses the thread on anything real. The difference between that ceiling and real mastery isn't better prompts. It's control. This workshop is about control.You'll spend the whole four hours inside CLASH, a real full-stack application, handed to you fully built so nothing stands between you and the hard parts. A serious codebase is the point: it's the only place agentic engineering shows you whether it actually holds up.The throughline is context. Treated carelessly, the context window fills with noise until the agent drifts. Treated as a resource you engineer, it becomes the biggest lever you have. From there the toolkit opens up. Repeatable work becomes a reusable Skill. Noisy, exploratory work goes to subagents that run in their own isolated context, several at once when the job allows. Hard rules become hooks the agent cannot cross. Your own systems come into reach through MCP.Then you let go of the wheel, carefully. The same agent that pairs with you can run headless in a pipeline, drive a long task to a defined finish on its own, or live inside your software through the Agent SDK. We close by setting two greenfield methodologies, Spec Kit and BMAD, side by side, so you leave knowing not just how to drive the agent but which approach fits which problem.Two ideas hold it together: context is king, and you push it, you own it. This was never about generating code faster. It's about staying in command while the agent does more.This is an advanced session for engineers, tech leads, and architects who already use Claude Code every day and want to reach the top of the curve. We move fast, and we start in the deep end.
Register
Agentic Design Systems: Making Design Systems Machine-Readable for MCP and LLMs
Upcoming
Agentic Design Systems: Making Design Systems Machine-Readable for MCP and LLMs
AI can generate UI. But it rarely generates the UI your design system would approve.

The issue isn’t capability — it’s missing structure.

Design systems today are optimized for humans: tokens, components, documentation, and guidelines. But the knowledge that governs product logic, accessibility constraints, and brand intent lives in scattered places, invisible to machines.

This talk introduces metadata as a semantic intelligence layer for frontend systems.

I’ll show how an auto-synced metadata architecture — powered by extractors and webhooks — continuously transforms codebases and design assets into structured knowledge consumable by MCP (Model Context Protocol) and LLMs.

We’ll explore the four knowledge layers required to make design systems machine-readable, why intent and context must be treated differently, and how this shift enables constraint-aware, accurate AI-generated UI.

Through a live end-to-end demo, you’ll see how structured metadata flows into MCP, delivers intent-based context, and produces reliable UI output.

We’ve spent years perfecting design systems for people.

This session explores what changes when we build them for machines.
Your Coding Agent Is Only as Good as Your Company’s Memory
Upcoming
Your Coding Agent Is Only as Good as Your Company’s Memory
Teams often start agent adoption by adding tools: MCP servers, API wrappers, chat interfaces in developer portals. But once coding agents move from local help into real delivery workflows, they usually fail earlier than the tool call. They read stale ownership, conflicting runbooks, missing deploy history, and policies that were written for humans.

This talk introduces company memory as the missing layer for production coding agents: trustworthy, permission-aware engineering context that agents can read, cite, and act on safely. We’ll look at what belongs in that memory, how to expose it through capability registries and machine-readable tool contracts, how to use on-behalf-of identity instead of shared AI service accounts, and how to grow trust from read → recommend → act.

The goal is simple: before giving agents more tools, make sure they can trust what they read.